





from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

_lowerCAmelCase								=  logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class  _UpperCAmelCase				(		_UpperCAmelCase  ):
      def __init__(		self   ,							*a__   ,							**a__							):
         super().__init__(*__UpperCamelCase   ,							**__UpperCamelCase							)
         self.check_model_type(__UpperCamelCase							)
      def 		_lowerCamelCase   (		self   ,							a__=None   ,							a__=None   ,							a__=None   ,							**a__							):
         A_   ,					A_  :					Union[str, Any]         =						{}, {}
         if padding is not None:
            A_  :					List[Any]         =						padding
         if truncation is not None:
            A_  :					List[str]         =						truncation
         if top_k is not None:
            A_  :					List[str]         =						top_k
         return preprocess_params, {}, postprocess_params
      def __call__(		self   ,							a__   ,							a__ = None   ,							**a__							):






         if isinstance(__UpperCamelCase   ,							(Image.Image, str)							) and isinstance(__UpperCamelCase   ,							__UpperCamelCase							):
            A_  :					Tuple         =						{"""image""": image, """question""": question}
         else:
            A_  :					Optional[Any]         =						image
         A_  :					Tuple         =						super().__call__(__UpperCamelCase   ,							**__UpperCamelCase							)
         return results
      def 		_lowerCamelCase   (		self   ,							a__   ,							a__=False   ,							a__=False							):
         A_  :					List[Any]         =						load_image(inputs["""image"""]							)
         A_  :					List[Any]         =						self.tokenizer(
             inputs["""question"""]   ,							return_tensors=self.framework   ,							padding=__UpperCamelCase   ,							truncation=__UpperCamelCase							)
         A_  :					int         =						self.image_processor(images=__UpperCamelCase   ,							return_tensors=self.framework							)
         model_inputs.update(__UpperCamelCase							)
         return model_inputs
      def 		_lowerCamelCase   (		self   ,							a__							):
         A_  :					str         =						self.model(**__UpperCamelCase							)
         return model_outputs






      def 		_lowerCamelCase   (		self   ,							a__   ,							a__=5							):






         if top_k > self.model.config.num_labels:
            A_  :					Tuple         =						self.model.config.num_labels

         if self.framework == "pt":
            A_  :					Optional[int]         =						model_outputs.logits.sigmoid()[0]
            A_   ,					A_  :					str         =						probs.topk(__UpperCamelCase							)
         else:
            raise ValueError(F"""Unsupported framework: {self.framework}"""							)

         A_  :					str         =						scores.tolist()
         A_  :					Dict         =						ids.tolist()
         return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__UpperCamelCase   ,							__UpperCamelCase							)]








"""simple docstring"""



import warnings
from functools import wraps
from typing import Callable


def 			_UpperCamelCase						(    _A   )     ->	Callable:





    """simple docstring"""

    @wraps(_A   )
    def _inner_fn(*_A       ,   **_A   ):
        warnings.warn(
            (F"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""")       ,   _A       ,   )
        return fn(*_A       ,   **_A   )

    return _inner_fn